Research Article Published: February 24, 2026 Volume 2, Issue 1

Intelligent Enterprise Process Orchestration: A Machine Learning-Driven Framework for Predictive Workflow Automation in CRM Platforms

Authors

S. Okeke1

Affiliation: Department of Computer Engineering, Centre of Excellence in Engineering, Nigeria.

Abstract

Customer Relationship Management (CRM) platforms have historically relied on static, rule-based automation. While stable, these systems lack the adaptability required for modern, high-velocity enterprise environments. This paper proposes a predictive workflow automation framework that transitions CRM systems from reactive tools to proactive, self-optimizing engines.
By integrating Machine Learning (ML) directly into the orchestration layer, the framework enables real-time decision-making based on probabilistic outcomes rather than deterministic rules. The architecture synthesizes concepts from Analytical CRM and Predictive Business Process Monitoring (PBPM), governed by the CRISP-ML(Q) lifecycle model to ensure model quality and reliability. Key application areas include predictive lead scoring, automated churn prevention, and dynamic task routing. The study further addresses critical implementation challenges such as model governance, integration latency, and data ethics, demonstrating that predictive automation significantly enhances operational efficiency and customer engagement responsiveness.

Keywords

Predictive Workflow Automation Analytical CRM Machine Learning Operations CRISP-ML(Q) Intelligent Process Automation Enterprise AI Churn Prediction Dynamic Case Routing

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How to Cite

APA Style:

Okeke, S. (2026). Intelligent enterprise process orchestration: A machine learning-driven framework for predictive workflow automation in CRM platforms. International Journal of Advanced Research in Engineering and Related Sciences, 2(1), 11-20. https://doi.org/10.5281/zenodo.17597201

IEEE Style:

S. Okeke, "Intelligent enterprise process orchestration: A machine learning-driven framework for predictive workflow automation in CRM platforms," International Journal of Advanced Research in Engineering and Related Sciences, vol. 2, no. 1, pp. 11-20, 2026. https://doi.org/10.5281/zenodo.17597201

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DOI

DOI: https://doi.org/10.5281/zenodo.17597201